Make data frame
Exclude Protocol 8 (mother)
Define Functions
## Func t test si boxplot simplu
func_t_box <- function(df, ind, pre_var, post_var, facet = FALSE, xlab = ""){
if(facet){
facet <- "Protocol"
}else{
facet <- NULL
}
df_modif <-
df %>%
select(ind, P, pre_var, post_var) %>%
tidyr::drop_na() %>%
gather(pre_var, post_var, key = "PrePost", value = "value") %>%
mutate_at(vars(c(1, 2)), funs(as.factor)) %>%
mutate(PrePost = factor(PrePost, levels = c(pre_var, post_var)))
if(!is.null(facet)){
df_modif <-
df_modif %>%
group_by(P) %>%
mutate(Protocol = paste0("Protocol = ", P, ", n = ", n()))
}
stat_comp <-
df_modif %>%
do(tidy(t.test(.$value ~ .$PrePost,
paired = TRUE,
data=.)))
plot <-
ggpubr::ggpaired(df_modif, x = "PrePost", y = "value", id = ind,
color = "PrePost", line.color = "gray", line.size = 0.4,
palette = c("#00AFBB", "#FC4E07"), legend = "none",
facet.by = facet, ncol = 3,
xlab = xlab) +
stat_summary(fun.data = mean_se, colour = "darkred") +
ggpubr::stat_compare_means(method = "t.test", paired = TRUE, label.x = as.numeric(df_modif$PrePost)-0.4, label.y = max(df_modif$value)+1) +
ggpubr::stat_compare_means(method = "t.test", paired = TRUE, label = "p.signif", comparisons = list(c(pre_var, post_var)))
print(stat_comp)
cat("\n")
print(plot)
cat("\n")
plot.new() # Need this workaround for interleaving tables and plots in R Markdown, within loop
dev.off()
}
heat_cor_plotly <- function(df, x_vars = NULL, y_vars = NULL, low_color = "cyan", high_color = "red", ...){
# inherit type = c("pearson","spearman") from Hmisc::rcorr()
library(ggplot2)
library(plotly)
library(reshape2)
library(Hmisc)
# use all numeric columns only, print message if non-numeric are found
numeric_cols <- unlist(lapply(df, is.numeric))
if(!all(numeric_cols)) message("Warning: Non-numeric columns were excluded!")
df <- df[, numeric_cols]
df_mat <- as.matrix(df)
rt <- Hmisc::rcorr(df_mat, ...)
# extract correlations, p-values and merge into another dataframe
mtlr <- reshape2::melt(rt$r, value.name = "Correlation")
mtlp <- reshape2::melt(rt$P, value.name = "P-Value")
mtl <- merge(mtlr, mtlp)
# give possibility to prune the correlation matrix
if(!is.null(x_vars)){
mtl <- mtl[(mtl$Var1 %in% x_vars), ]
}
if(!is.null(x_vars)){
mtl <- mtl[(mtl$Var2 %in% y_vars), ]
}
# want to avoid scientific notetion, but this doesnt work as numeric
# mtl$Correlation <- as.numeric(format(mtl$Correlation, digits = 4, scientific = FALSE)) # doesnt work
# mtl$`P-Value` <- as.numeric(format(mtl$`P-Value`, digits = 4, scientific = FALSE))
options(scipen = 999)
mtl$Correlation <- round(mtl$Correlation, 3)
mtl$`P-Value` <- round(mtl$`P-Value`, 3)
gx <-
ggplot2::ggplot(mtl,
aes(Var1, Var2,
fill = Correlation,
text = paste("P-val = ", `P-Value`))) +
ggplot2::geom_tile() +
ggplot2::scale_fill_gradient(low = low_color, high = high_color, limits = c(-1, 1), breaks = c(-1, -.5, 0, .5, 1)) +
ggplot2::theme_minimal() +
{if(any(nchar(names(df)) > 6)) ggplot2::theme(axis.text.x = element_text(angle = 90, hjust = 1))} # vertical x axis labels if lenghty
plotly::ggplotly(gx)
}
Plot
## Dodged Bar plot of Age and Gender
Data %>%
mutate(Varta_categ = cut(Varsta,
breaks=c(-Inf, 25, 30, 35, 40, 45, 50, 55, 60, Inf),
labels=c("<25","25-29","30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60>"),
right = FALSE)) %>%
mutate(Varsta = as.factor(Varsta),
Gen = as.factor(as.character(Gen))) %>%
mutate(Gen = forcats::fct_recode(Gen, "femin" = "1", "masculin" = "2")) %>%
dplyr::count(Varta_categ, Gen) %>% # Group by, then count number in each group
mutate(pct = prop.table(n)) %>% # Calculate percent within each var
ggplot(aes(x = Varta_categ, y = pct, fill = Gen, label = scales::percent(pct))) +
geom_col(position = 'dodge') +
geom_text(position = position_dodge(width = .9), # move to center of bars
vjust = -0.5, # nudge above top of bar
size = 3) +
scale_y_continuous(labels = scales::percent) +
ggtitle("") +
xlab("Varsta") + ylab("Percentage %") +
guides(fill = guide_legend(title = "Gen", ncol = 1)) +
scale_fill_grey(start = 0.8, end = 0.2, na.value = "red", aesthetics = "fill") +
theme(legend.position = "right", legend.direction = "vertical",
legend.justification = c(0, 1), panel.border = element_rect(fill = NA, colour = "black"))

## Pie chart
Data %>%
mutate(Gen = as.factor(as.character(Gen))) %>%
mutate(Gen = forcats::fct_recode(Gen, "femin" = "1", "masculin" = "2")) %>%
group_by(Gen) %>%
dplyr::summarise(counts = n()) %>%
mutate(prop = round(counts*100/sum(counts), 1),
lab.ypos = cumsum(prop) - .5*prop,
Percent = paste0(prop, " %")) %>%
ggpubr::ggpie(x = "prop", label = "Percent",
fill = "Gen", color = "white",
lab.pos = "in", lab.font = list(color = "white"),
palette = "grey")

Analyses
Simple before-after analyses with t test
VAS Stress
NANA

null device 1
VAS Stress
NANA

null device 1
Correlations: Anotimpuri - Calitate Amintiri (without P6, P7)
dateplot1 <- Data[, c("P", "Primavara", "Vara", "Toamna", "Iarna", "Media_s1", "Media_s2", "Media_s3", "SocDih_Part", "SocDih_FamN", "SocDih_FamInd", "SocDih_Priet", "SocDih_Amici", "SocDih_Necun", "SocDih_Antag", "SocDih_TotAprop", "SocDih_TotNeaprop", "STAI_T")]
names(dateplot1) <- c("P", "Primavara", "Vara", "Toamna", "Iarna", "S1- Valenta", "S2 - Vividness", "S3 - Relevanta", "Partener", "Familie nucleu", "Familie extinsa", "Prieteni", "Amici", "Necunoscuti", "Antagonisti", "Toti Apropiatii", "Toti Neapropiatii", "STAI_T")
dateplot1 <- subset(dateplot1, P!=6 & P!=7)
COR <- Hmisc::rcorr(as.matrix(dateplot1[,-1]))
M <- COR$r
P_MAT <- COR$P
corrplot::corrplot(M, method = "number", type = "upper", p.mat = P_MAT, sig.level = 0.05, insig = "blank", tl.col = "black", tl.cex = .9, tl.srt = 45)

Correlations: Personality - Qualities of Memories (without P6, P7)
dateplot2 <- Data[, c(24, 40, 56, 87:121, 126)]
names(dateplot2)[1:3] <- c("S1- Valenta", "S2 - Vividness", "S3 - Relevanta")
COR <- Hmisc::rcorr(as.matrix(dateplot2))
M <- COR$r
P_MAT <- COR$P
corrplot::corrplot(M, type = "upper", p.mat = P_MAT, sig.level = 0.05, insig = "blank", tl.col = "black", tl.cex = .7, cl.pos = "b", tl.srt = 45)

Correlations: Social - Personality
dateplot3 <- Data[, c(131:139, 87:121)]
names(dateplot3)[1:9] <- c("Partener", "Familie nucleu", "Familie extinsa", "Prieteni", "Amici", "Necunoscuti", "Antagonisti", "Toti Apropiatii", "Toti Neapropiatii")
COR <- Hmisc::rcorr(as.matrix(dateplot3))
M <- COR$r
P_MAT <- COR$P
corrplot::corrplot(M, type = "upper", p.mat = P_MAT, sig.level = 0.05, insig = "blank", tl.col = "black", tl.cex = .7, cl.pos = "b", tl.srt = 45)

Session Info
R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 8.1 x64 (build 9600)
Matrix products: default
locale:
[1] LC_COLLATE=Romanian_Romania.1250 LC_CTYPE=Romanian_Romania.1250 LC_MONETARY=Romanian_Romania.1250 LC_NUMERIC=C
[5] LC_TIME=Romanian_Romania.1250
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Hmisc_4.1-1 Formula_1.2-3 survival_2.44-1.1 lattice_0.20-38 reshape2_1.4.3
[6] plotly_4.8.0 rio_0.5.16 scales_1.0.0 ggpubr_0.2 magrittr_1.5
[11] tadaatoolbox_0.16.1 summarytools_0.8.8 rstatix_0.2.0 broom_0.5.2 PerformanceAnalytics_1.5.2
[16] xts_0.11-2 zoo_1.8-4 psych_1.8.12 plyr_1.8.4 forcats_0.4.0
[21] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2 readr_1.3.1 tidyr_1.0.0
[26] tibble_2.1.3 ggplot2_3.2.1 tidyverse_1.2.1 papaja_0.1.0.9842 pacman_0.5.1
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 ggsignif_0.4.0 pryr_0.1.4 ellipsis_0.2.0.1 htmlTable_1.12 base64enc_0.1-3 rstudioapi_0.8
[8] DT_0.5 manipulate_1.0.1 mvtnorm_1.0-11 lubridate_1.7.4 xml2_1.2.0 splines_3.6.1 codetools_0.2-16
[15] mnormt_1.5-5 knitr_1.24 zeallot_0.1.0 pixiedust_0.8.6 jsonlite_1.6 cluster_2.1.0 shiny_1.2.0
[22] compiler_3.6.1 httr_1.4.0 backports_1.1.4 assertthat_0.2.1 Matrix_1.2-17 lazyeval_0.2.2 cli_1.1.0
[29] later_0.7.5 acepack_1.4.1 htmltools_0.3.6 tools_3.6.1 gtable_0.3.0 glue_1.3.1 Rcpp_1.0.2
[36] carData_3.0-2 cellranger_1.1.0 vctrs_0.2.0 nlme_3.1-140 crosstalk_1.0.0 xfun_0.9 openxlsx_4.1.0
[43] rvest_0.3.2 mime_0.7 lifecycle_0.1.0 MASS_7.3-51.4 hms_0.5.1 promises_1.0.1 parallel_3.6.1
[50] expm_0.999-3 RColorBrewer_1.1-2 pwr_1.2-2 yaml_2.2.0 curl_3.2 gridExtra_2.3 pander_0.6.3
[57] rpart_4.1-15 latticeExtra_0.6-28 stringi_1.4.3 corrplot_0.84 nortest_1.0-4 checkmate_1.8.5 boot_1.3-22
[64] zip_1.0.0 rlang_0.4.0 pkgconfig_2.0.2 matrixStats_0.54.0 bitops_1.0-6 rapportools_1.0 htmlwidgets_1.3
[71] labeling_0.3 tidyselect_0.2.5 R6_2.4.0 DescTools_0.99.28 generics_0.0.2 pillar_1.4.2 haven_2.1.1
[78] foreign_0.8-71 withr_2.1.2 nnet_7.3-12 abind_1.4-5 RCurl_1.95-4.11 modelr_0.1.5 crayon_1.3.4
[85] car_3.0-2 viridis_0.5.1 grid_3.6.1 readxl_1.1.0 data.table_1.11.8 digest_0.6.20 xtable_1.8-4
[92] httpuv_1.4.5 munsell_0.5.0 viridisLite_0.3.0 quadprog_1.5-5
A work by Claudiu Papasteri
claudiu.papasteri@gmail.com
---
title: "<br> General Plots for M.1. (Autobiographical Memories)" 
subtitle: "Initial Dataset"
author: "<br> Claudiu Papasteri"
date: "`r format(Sys.time(), '%d %m %Y')`"
output: 
    html_notebook:
            code_folding: hide
            toc: true
            toc_depth: 2
            number_sections: true
            theme: spacelab
            highlight: tango
            font-family: Arial
            fig_width: 10
            fig_height: 9
    # word_document        
    # pdf_document: 
            # toc: true
            # toc_depth: 2
            # number_sections: true
            # fontsize: 11pt
            # geometry: margin=1in
            # fig_width: 7
            # fig_height: 6
            # fig_caption: true
    # github_document: 
            # toc: true
            # toc_depth: 2
            # html_preview: false
            # fig_width: 5
            # fig_height: 5
            # dev: jpeg
---


<!-- Setup -->


```{r setup, include=FALSE}
# kintr options
knitr::opts_chunk$set(
  comment = "#",
  collapse = TRUE,
  echo = TRUE, 
  warning = FALSE, message = FALSE, error = FALSE,
  cache = TRUE       # echo = False for github_document, but will be folded in html_notebook
)

# General R options and info
set.seed(111)               # in case we use randomized procedures       
options(scipen = 999)       # positive values bias towards fixed and negative towards scientific notation

# Load packages
if (!require("pacman")) install.packages("pacman")
packages <- c(
  "papaja",
  "tidyverse", "plyr",      
  "psych", "PerformanceAnalytics",          
  "broom", "rstatix",
  "summarytools", "tadaatoolbox",           
  "ggplot2", "ggpubr", "scales",        
  "rio"
  # , ...
)
if (!require("pacman")) install.packages("pacman")
pacman::p_load(char = packages)

# Themes for ggplot2 ploting (here used APA style)
theme_set(theme_apa())

# Tables knitting to Word
doc.type <- knitr::opts_knit$get('rmarkdown.pandoc.to')  # then format tables using an if statement like:
# if (doc.type == "docx") { pander::pander(df) } else { knitr::kable(df) }

# Set wd for Notebook
folder <- "C:/Users/Mihai/Desktop/R Notebooks/notebooks/M.1. General"
# knitr::opts_knit$set(root.dir = normalizePath(folder))
```





<!-- Report -->


# Read

```{r red_clean_recode_merge, results='hide'}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Read
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## Read files
file <- "Date Complete M1 v.13 siPPGGSRamilaza.sav"

# setwd(folder)
Data <- rio::import(file.path(folder, file))
```


# Make data frame

```{r df_excel}
Data %>%
  dplyr::select(-Nume) %>%
    DT::datatable(
      extensions = 'Buttons',
      options = list(pageLength = 10,
                     scrollX='500px',
                     dom = 'Bfrtip',
                     buttons = c('excel', "csv")))
```


## Exclude Protocol 8 (mother)

```{r df_filtered}
Data <- 
  Data %>%
  filter(P != 8)
```


# Define Functions 

```{r def_func_ttest, hide=TRUE, results='asis'}
## Func t test si boxplot simplu
func_t_box <- function(df, ind, pre_var, post_var, facet = FALSE, xlab = ""){  
  if(facet){
    facet <- "Protocol"
  }else{
    facet <- NULL
  }
  
  df_modif <-
    df %>%
    select(ind, P, pre_var, post_var) %>% 
    tidyr::drop_na() %>%
    gather(pre_var, post_var, key = "PrePost", value = "value") %>% 
    mutate_at(vars(c(1, 2)), funs(as.factor)) %>% 
    mutate(PrePost = factor(PrePost, levels = c(pre_var, post_var))) 
  
  if(!is.null(facet)){
    df_modif <-
      df_modif %>%
      group_by(P) %>%
      mutate(Protocol = paste0("Protocol = ", P, ", n = ", n()))
  }
  
  stat_comp <-
    df_modif %>% 
    do(tidy(t.test(.$value ~ .$PrePost,
                   paired = TRUE,
                   data=.)))
  
  plot <- 
    ggpubr::ggpaired(df_modif, x = "PrePost", y = "value", id = ind, 
                     color = "PrePost", line.color = "gray", line.size = 0.4,
                     palette = c("#00AFBB", "#FC4E07"), legend = "none",
                     facet.by = facet, ncol = 3, 
                     xlab = xlab) +
    stat_summary(fun.data = mean_se,  colour = "darkred") +
    ggpubr::stat_compare_means(method = "t.test", paired = TRUE, label.x = as.numeric(df_modif$PrePost)-0.4, label.y = max(df_modif$value)+1) + 
    ggpubr::stat_compare_means(method = "t.test", paired = TRUE, label = "p.signif", comparisons = list(c(pre_var, post_var)))
  
  print(stat_comp)
  cat("\n")                      
  print(plot)
  cat("\n")
  plot.new()                     # Need this workaround for interleaving tables and plots in R Markdown, within loop
  dev.off()
}
```


```{r def_func_heatcorplotly, hide=TRUE, results='asis'}
heat_cor_plotly <- function(df, x_vars = NULL, y_vars = NULL, low_color = "cyan",  high_color = "red",  ...){   
  # inherit type = c("pearson","spearman") from Hmisc::rcorr() 
  library(ggplot2)
  library(plotly)
  library(reshape2)
  library(Hmisc)
  
  # use all numeric columns only, print message if non-numeric are found
  numeric_cols <- unlist(lapply(df, is.numeric))
  if(!all(numeric_cols)) message("Warning: Non-numeric columns were excluded!")
  df <- df[, numeric_cols]
  
  df_mat <- as.matrix(df)
  rt <- Hmisc::rcorr(df_mat, ...)
  
  # extract correlations, p-values and merge into another dataframe
  mtlr <- reshape2::melt(rt$r, value.name = "Correlation")
  mtlp <- reshape2::melt(rt$P, value.name = "P-Value")
  
  mtl <- merge(mtlr, mtlp)
  
  # give possibility to prune the correlation matrix
  if(!is.null(x_vars)){
    mtl <- mtl[(mtl$Var1 %in% x_vars), ]
  }
  if(!is.null(x_vars)){
    mtl <- mtl[(mtl$Var2 %in% y_vars), ]
  }
  
  # want to avoid scientific notetion, but this doesnt work as numeric
  # mtl$Correlation <- as.numeric(format(mtl$Correlation, digits = 4, scientific = FALSE))  # doesnt work
  # mtl$`P-Value` <- as.numeric(format(mtl$`P-Value`, digits = 4, scientific = FALSE)) 
  options(scipen = 999)
  mtl$Correlation <- round(mtl$Correlation, 3)
  mtl$`P-Value` <- round(mtl$`P-Value`, 3)

  gx <-
    ggplot2::ggplot(mtl, 
           aes(Var1, Var2, 
               fill = Correlation,  
               text = paste("P-val = ", `P-Value`))) +
    ggplot2::geom_tile() + 
    ggplot2::scale_fill_gradient(low = low_color,  high = high_color, limits = c(-1, 1), breaks = c(-1, -.5, 0, .5, 1)) +
    ggplot2::theme_minimal() +
    {if(any(nchar(names(df)) > 6)) ggplot2::theme(axis.text.x = element_text(angle = 90, hjust = 1))}  # vertical x axis labels if lenghty
  plotly::ggplotly(gx)  
}
```


# Plot

```{r plot1, fig.width=8, fig.height=6, results='asis'}
## Dodged Bar plot of Age and Gender
Data  %>%
  mutate(Varta_categ = cut(Varsta, 
                           breaks=c(-Inf, 25, 30, 35, 40, 45, 50, 55, 60, Inf), 
                           labels=c("<25","25-29","30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60>"), 
                           right = FALSE)) %>%  
  mutate(Varsta = as.factor(Varsta),
         Gen = as.factor(as.character(Gen))) %>%
  mutate(Gen = forcats::fct_recode(Gen, "femin" = "1", "masculin" = "2")) %>%
  dplyr::count(Varta_categ, Gen) %>%                        # Group by, then count number in each group
  mutate(pct = prop.table(n)) %>%                     # Calculate percent within each var
  ggplot(aes(x = Varta_categ, y = pct, fill = Gen, label = scales::percent(pct))) + 
    geom_col(position = 'dodge') + 
    geom_text(position = position_dodge(width = .9),    # move to center of bars
              vjust = -0.5,                             # nudge above top of bar
              size = 3) + 
    scale_y_continuous(labels = scales::percent) +
    ggtitle("") +
    xlab("Varsta") + ylab("Percentage %") + 
    guides(fill = guide_legend(title = "Gen", ncol = 1)) + 
    scale_fill_grey(start = 0.8, end = 0.2, na.value = "red", aesthetics = "fill") +
    theme(legend.position = "right", legend.direction = "vertical", 
          legend.justification = c(0, 1), panel.border = element_rect(fill = NA, colour = "black"))
```


```{r plot2, fig.width=6, fig.height=6, results='asis'}
## Pie chart
Data  %>%
  mutate(Gen = as.factor(as.character(Gen))) %>%
  mutate(Gen = forcats::fct_recode(Gen, "femin" = "1", "masculin" = "2")) %>%
  group_by(Gen) %>%
  dplyr::summarise(counts = n()) %>%
  mutate(prop = round(counts*100/sum(counts), 1),
         lab.ypos = cumsum(prop) - .5*prop,
         Percent = paste0(prop, " %")) %>% 
  ggpubr::ggpie(x = "prop", label = "Percent",
                fill = "Gen", color = "white", 
                lab.pos = "in", lab.font = list(color = "white"),
                palette = "grey")
```



# Analyses

## Simple before-after analyses with t test

```{r t_test1, fig.width=5, fig.height=6, results='asis'}
## Simple before-after analyses with t test
cat("#### VAS Stress")
func_t_box(Data, ind = "ID", "Stres_pre", "Stres_post", facet = FALSE) 
```


```{r t_test2, fig.width=8, fig.height=12, results='asis'}
## Simple before-after analyses with t test
cat("#### VAS Stress")
func_t_box(Data, ind = "ID", "Stres_pre", "Stres_post", facet = TRUE) 
```


## Correlations: Anotimpuri - Calitate Amintiri (without P6, P7)

```{r cor1, fig.width=9, fig.height=9, results='asis'}
dateplot1 <- Data[, c("P", "Primavara", "Vara", "Toamna", "Iarna", "Media_s1", "Media_s2", "Media_s3",  "SocDih_Part",  "SocDih_FamN",  "SocDih_FamInd",  "SocDih_Priet",  "SocDih_Amici",  "SocDih_Necun",  "SocDih_Antag",  "SocDih_TotAprop",  "SocDih_TotNeaprop", "STAI_T")] 
names(dateplot1) <- c("P", "Primavara", "Vara", "Toamna", "Iarna", "S1- Valenta", "S2 - Vividness", "S3 - Relevanta",  "Partener",  "Familie nucleu",  "Familie extinsa",  "Prieteni",  "Amici",  "Necunoscuti",  "Antagonisti",  "Toti Apropiatii",  "Toti Neapropiatii", "STAI_T")
dateplot1 <- subset(dateplot1, P!=6 & P!=7)

COR <- Hmisc::rcorr(as.matrix(dateplot1[,-1]))   
M <- COR$r
P_MAT <- COR$P
corrplot::corrplot(M, method = "number", type = "upper", p.mat = P_MAT, sig.level = 0.05, insig = "blank", tl.col = "black", tl.cex = .9, tl.srt = 45)  
```


```{r heat_cor1, fig.width=9, fig.height=9, results='asis'}
heat_cor_plotly(dateplot1[,-1])
```


## Correlations: Personality - Qualities of Memories (without P6, P7)

```{r cor2, fig.width=11, fig.height=11, results='asis'}
dateplot2 <- Data[, c(24, 40, 56, 87:121, 126)] 
names(dateplot2)[1:3] <- c("S1- Valenta", "S2 - Vividness", "S3 - Relevanta")

COR <- Hmisc::rcorr(as.matrix(dateplot2))   
M <- COR$r
P_MAT <- COR$P
corrplot::corrplot(M, type = "upper", p.mat = P_MAT, sig.level = 0.05, insig = "blank", tl.col = "black", tl.cex = .7, cl.pos = "b", tl.srt = 45)
```


```{r heat_cor2, fig.width=4, fig.height=12, results='asis'}
heat_cor_plotly(dateplot2, x_vars = names(dateplot2)[1:3], y_vars = names(dateplot2)[-(1:3)])
```


## Correlations: Social - Personality

```{r cor3, fig.width=11, fig.height=11, results='asis'}
dateplot3 <- Data[, c(131:139, 87:121)]
names(dateplot3)[1:9] <- c("Partener",  "Familie nucleu",  "Familie extinsa",  "Prieteni",  "Amici",  "Necunoscuti",  "Antagonisti",  "Toti Apropiatii",  "Toti Neapropiatii")

COR <- Hmisc::rcorr(as.matrix(dateplot3))   
M <- COR$r
P_MAT <- COR$P
corrplot::corrplot(M, type = "upper", p.mat = P_MAT, sig.level = 0.05, insig = "blank", tl.col = "black", tl.cex = .7, cl.pos = "b", tl.srt = 45)
```


```{r heat_cor3, fig.width=6, fig.height=12, results='asis'}
heat_cor_plotly(dateplot3, x_vars = names(dateplot3)[1:9], y_vars = names(dateplot3)[-(1:9)])
```






<br>





<!-- Session Info and License -->

<br>

# Session Info
```{r session_info, echo = FALSE, results = 'markup'}
sessionInfo()    
```

<!-- Footer -->
&nbsp;
<hr />
<p style="text-align: center;">A work by <a href="https://github.com/ClaudiuPapasteri/">Claudiu Papasteri</a></p>
<p style="text-align: center;"><span style="color: #808080;"><em>claudiu.papasteri@gmail.com</em></span></p>
&nbsp;
